{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import seaborn as sns\n",
    "\n",
    "train_path = \"../../../data/train.txt\"\n",
    "test_path = \"../../../data/test.txt\"\n",
    "attribute_path = \"../../../data/itemAttribute.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_line = 0 \n",
    "end_line = 0\n",
    "user_item_list = {}\n",
    "rating_list = []\n",
    "rating_num = 0\n",
    "\n",
    "with open(train_path, 'r') as file: \n",
    "    train_lines = file.readlines()\n",
    "\n",
    "for index, line in enumerate(train_lines):\n",
    "    if '|' in line:\n",
    "        userid, num_rating_item = line.split('|')\n",
    "        userid = int(userid)\n",
    "        start_line = index + 1\n",
    "        end_line = index + int(num_rating_item)\n",
    "        rating_num = rating_num + int(num_rating_item)\n",
    "        user_item_list[userid] = {}\n",
    "    elif index >= start_line and index <= end_line:\n",
    "        line = line.strip('\\n')\n",
    "        item, rating = line.split(\"  \")\n",
    "        user_item_list[userid][item] = int(rating)\n",
    "        rating_list.append(int(rating))\n",
    "\n",
    "user_num = len(user_item_list)\n",
    "max_user_id = max(user_item_list.keys())\n",
    "min_user_id = min(user_item_list.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "item_user_list = {}\n",
    "for userid, item_list in user_item_list.items():\n",
    "    for item in item_list:\n",
    "        item = int(item)\n",
    "        item_user_list.setdefault(item, dict())\n",
    "        item_user_list[item][userid] = item_list[str(item)]\n",
    "\n",
    "item_num = len(item_user_list)\n",
    "max_item_id = max(item_user_list.keys())\n",
    "min_item_id = min(item_user_list.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_avg = 0.0\n",
    "avg_list = []\n",
    "for userid, item_list in user_item_list.items():\n",
    "    avg = 0.0\n",
    "    for item, rating_score in item_list.items():\n",
    "        avg += rating_score\n",
    "        all_avg += rating_score\n",
    "    avg = avg / len(item_list)\n",
    "    avg_list.append(avg)\n",
    "\n",
    "all_avg = all_avg / rating_num\n",
    "\n",
    "item_avg_list = []\n",
    "for item, user_list in item_user_list.items():\n",
    "    avg = 0.0\n",
    "    for userid, rating_score in user_list.items():\n",
    "        avg += rating_score\n",
    "    avg = avg / len(user_list)\n",
    "    item_avg_list.append(avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(attribute_path, 'r') as file: \n",
    "    attr_lines = file.readlines()\n",
    "\n",
    "all_miss_num = 0\n",
    "all_hit_num = 0\n",
    "attr1_hit_num = 0\n",
    "attr2_hit_num = 0\n",
    "attr1_sum = 0\n",
    "attr2_sum = 0\n",
    "\n",
    "for line in attr_lines:\n",
    "    line = line.strip('\\n')\n",
    "    itemid, attr1, attr2 = line.split('|')\n",
    "    if attr1 == \"None\" and attr2 == \"None\":\n",
    "        all_miss_num = all_miss_num + 1\n",
    "    elif attr1 == \"None\" and attr2 != \"None\":\n",
    "        attr2_hit_num = attr2_hit_num + 1\n",
    "        attr2_sum = attr2_sum + int(attr2)\n",
    "    elif attr1 != \"None\" and attr2 == \"None\":\n",
    "        attr1_hit_num = attr1_hit_num + 1\n",
    "        attr1_sum = attr1_sum + int(attr1)\n",
    "    else:\n",
    "        all_hit_num = all_hit_num + 1\n",
    "        attr1_sum = attr1_sum + int(attr1)\n",
    "        attr2_sum = attr2_sum + int(attr2)\n",
    "\n",
    "attr_len =  len(attr_lines)\n",
    "attr1_avg = attr1_sum / (all_hit_num + attr1_hit_num)\n",
    "attr2_avg = attr2_sum / (all_hit_num + attr2_hit_num)\n",
    "categories = ['Attr1+Attr2', 'Attr1', 'Attr2', 'All_None']\n",
    "values = [all_hit_num, attr1_hit_num, attr2_hit_num, all_miss_num]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(test_path, 'r') as file: \n",
    "    test_lines = file.readlines()\n",
    "\n",
    "start_line = 0 \n",
    "end_line = 0\n",
    "test_user_item_list = {}\n",
    "train_miss_item_num = 0\n",
    "    \n",
    "for index, line in enumerate(test_lines):\n",
    "    if '|' in line:\n",
    "        userid, num_rating_item = line.split('|')\n",
    "        start_line = index + 1\n",
    "        end_line = index + int(num_rating_item)\n",
    "        test_user_item_list[userid] = {}\n",
    "    elif index >= start_line and index <= end_line:\n",
    "        line = line.strip('\\n')\n",
    "        line = int(line)\n",
    "        test_user_item_list[userid][line] = int(-1)\n",
    "        if line not in item_user_list:\n",
    "            train_miss_item_num = train_miss_item_num + 1\n",
    "\n",
    "test_user_num = len(test_user_item_list)\n",
    "test_item_num = len(test_lines) - test_user_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集中参与评分用户人数：19835\n",
      "训练集中参与评分用户最大编号：19834\n",
      "训练集中参与评分用户最小编号：0\n",
      "训练集中参与评分物品数量：455691\n",
      "训练集中参与评分物品最大编号：624960\n",
      "训练集中参与评分物品最小编号：0\n",
      "训练集中未被评分物品数量：169269\n",
      "训练集中评分总数：5001507\n",
      "训练集中总评分均值：49.50627100991761\n",
      "属性集中物品属性条数：507172\n",
      "属性集中属性1的平均值：314596.1235557028\n",
      "属性集中属性2的平均值：311482.46067610726\n",
      "测试集要求预测评分用户数目：19835\n",
      "测试集要求预测评分物品数目：119010\n",
      "测试集中未在训练集评分物品数量：1040\n"
     ]
    }
   ],
   "source": [
    "print(f'训练集中参与评分用户人数：{user_num}')\n",
    "print(f'训练集中参与评分用户最大编号：{max_user_id}')\n",
    "print(f'训练集中参与评分用户最小编号：{min_user_id}')\n",
    "print(f'训练集中参与评分物品数量：{item_num}')\n",
    "print(f'训练集中参与评分物品最大编号：{max_item_id}')\n",
    "print(f'训练集中参与评分物品最小编号：{min_item_id}')\n",
    "print(f'训练集中未被评分物品数量：{max_item_id - item_num}')\n",
    "print(f'训练集中评分总数：{rating_num}')\n",
    "print(f'训练集中总评分均值：{all_avg}')\n",
    "print(f'属性集中物品属性条数：{attr_len}')\n",
    "print(f'属性集中属性1的平均值：{attr1_avg}')\n",
    "print(f'属性集中属性2的平均值：{attr2_avg}')\n",
    "print(f'测试集要求预测评分用户数目：{test_user_num}')\n",
    "print(f'测试集要求预测评分物品数目：{test_item_num}')\n",
    "print(f'测试集中未在训练集评分物品数量：{train_miss_item_num}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams['font.family'] = 'SimHei'\n",
    "\n",
    "fig, axes = plt.subplots(2, 2, figsize=(12, 12))\n",
    "\n",
    "sns.histplot(rating_list, bins=40, kde=False, ax=axes[0, 0])\n",
    "axes[0, 0].set_title('训练集评分直方图')\n",
    "axes[0, 0].set_xlabel('评分')\n",
    "axes[0, 0].set_ylabel('数目')\n",
    "\n",
    "sns.histplot(avg_list, bins=40, kde=True, ax=axes[0, 1])\n",
    "axes[0, 1].set_title('训练集用户打分均值直方图')\n",
    "axes[0, 1].set_xlabel('用户打分均值')\n",
    "axes[0, 1].set_ylabel('用户人数')\n",
    "\n",
    "sns.histplot(item_avg_list, bins=40, kde=True, ax=axes[1, 0])\n",
    "axes[1, 0].set_title('训练集物品评分均值直方图')\n",
    "axes[1, 0].set_xlabel('物品评分均值')\n",
    "axes[1, 0].set_ylabel('物品数目')\n",
    "\n",
    "bars = axes[1, 1].bar(categories, values, color=['blue', 'green', 'red', 'orange'])\n",
    "axes[1, 1].set_title('属性统计图')\n",
    "axes[1, 1].set_xlabel('属性')\n",
    "axes[1, 1].set_ylabel('数目')\n",
    "\n",
    "for bar in bars:\n",
    "    height = bar.get_height()\n",
    "    axes[1, 1].annotate(f'{height}',\n",
    "        xy=(bar.get_x() + bar.get_width() / 2, height),\n",
    "        xytext=(0, 2), \n",
    "        textcoords=\"offset points\",\n",
    "        ha='center', va='bottom')\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  }
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